SMART GRIDS AND RENEWABLES
A COST-BENEFIT ANALYSIS GUIDE
FOR DEVELOPING COUNTRIES
POWER SECTOR
TRANSFORMATION
Copyright © IRENA 2015
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About IRENA
The International Renewable Energy Agency (IRENA) is an intergovernmental organisation that supports
countries in their transition to a sustainable energy future, and serves as the principal platform for international cooperation, a centre of excellence, and a repository of policy, technology, resource and financial
knowledge on renewable energy. IRENA promotes the widespread adoption and sustainable use of all
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the pursuit of sustainable development, energy access, energy security and low-carbon economic growth
and prosperity.
Acknowledgements
The work for the preparation of this paper was led by Paul Komor and Anderson Hoke, University of Colorado, Boulder, Colorado, USA. The report has benefited from valuable comments provided by external
reviewers: Gianluca Flego (JRC, EC), Klas Heising (GIZ), Sophie Jablonski (EIB), Yannick Julliard (Siemens),
Achim Neumann (KFW), Juan Roberto Paredes (IDB), and Manuel Welsch (KTH).
Authors: Ruud Kempener (IRENA), Paul Komor and Anderson Hoke (University of Colorado)
For further information or to provide feedback, please contact: Ruud Kempener, IRENA Innovation and
Technology Centre. E-mail: RKempener@irena.org or secretariat@irena.org.
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Contents
EXECUTIVE SUMMARY������������������������������������������������������������������������������������������������������������������������������������������������������������������3
CHAPTER 1: RENEWABLES, SMART GRIDS AND COST-BENEFIT ANALYSIS�������������������������������������������������������� 4
Smart Grid Projects Need Careful Evaluation���������������������������������������������������������������������������������������������������������������� 6
Smart Grids in the Developing World������������������������������������������������������������������������������������������������������������������������������� 6
CHAPTER 2: COST-BENEFIT ANALYSIS: INTRODUCTION AND OVERVIEW����������������������������������������������������������8
Cost-Benefit Analysis in Context�����������������������������������������������������������������������������������������������������������������������������������������8
Considerations Before Undertaking a Cost-Benefit Analysis���������������������������������������������������������������������������������� 9
CHAPTER 3: SMART GRID COST-BENEFIT ANALYSIS FOR RURITANIA�����������������������������������������������������������������14
Ruritania Case Study Summary�����������������������������������������������������������������������������������������������������������������������������������������14
Step 1: Define Project�������������������������������������������������������������������������������������������������������������������������������������������������������������� 17
Step 2: Map Technologies to Functions���������������������������������������������������������������������������������������������������������������������������19
Step 3: Map Functions to Benefits������������������������������������������������������������������������������������������������������������������������������������20
Step 4: Monetise Benefits����������������������������������������������������������������������������������������������������������������������������������������������������20
Step 5: Quantify Costs����������������������������������������������������������������������������������������������������������������������������������������������������������� 27
Step 6: Compare Costs and Benefits�������������������������������������������������������������������������������������������������������������������������������29
Step 7: Sensitivity Analysis��������������������������������������������������������������������������������������������������������������������������������������������������30
SUMMARY���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 32
LIST OF ABBREVIATIONS����������������������������������������������������������������������������������������������������������������������������������������������������������34
REFERENCES����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������36
Executive Summary
Smart grid technologies can enable higher levels of renewables in electricity systems by making the system more
flexible, responsive, and intelligent. As more and more countries, particularly in the developing world, plan to
increase their use of renewables, smart grid technologies provide the means to integrate these renewables in a
cost-efficient and effective way.
Smart grid projects are often evaluated and justified on an economic basis. The challenge for decision-makers
(which can be utilities, policymakers, or others) is to evaluate smart grid proposals rigorously, objectively, and
with a well-defined and consistent methodology. Such analyses are critical for ensuring that scarce capital is
invested wisely.
Several methodologies exist for economic evaluation of smart grid projects. However, developing countries can
benefit from a customised methodology for smart grid project evaluation. This report provides a cost-benefit
analysis (CBA) methodology that is designed for developing countries. The proposed methodology allows for
analysis of benefits such as reduced theft, grid extension, and significant increases in reliability, and is realistic
about system data availability and accuracy.
CBA is an ideal first tool for evaluating a smart grid investment. Its value lies not just in the result it provides
but also in how it requires one to define and quantify the expected costs and benefits. Often it is this analytical
discipline, rather than the result itself, that is most informative.
Before undertaking a CBA, one needs to consider several issues. First, different stakeholders will value the
benefits of a smart grid differently. A societal perspective will account for all benefits; however, one may want to
take a narrower perspective, such as that of the utility or of electricity users. Second, undertaking a CBA requires
careful definition of a baseline, documenting what would happen in the absence of the smart grid project. Third,
CBA requires considerable judgment on the part of the analyst, particularly in estimating uncertain inputs and
assessing qualitative benefits. CBA can support better decisions, but it should not be used to make decisions on
its own.
This report is accompanied by a number of exercises to demonstrate the methodology and the value of CBA.
The fictional country of Ruritania1 is used to demonstrate a CBA of smart inverters for renewables in a small
developing country’s electricity system. The results reveal that fewer outages and reduced losses are by far the
most valuable benefits of these inverters, accounting for almost three-fourths of the total benefit value. The
advantages of smart inverters clearly exceed the costs. This result, however, reflects electricity users’ estimated
valuation of reduced outages. If the utility values fewer outages only at the value of the lost electricity sales, then
the smart inverters are not cost-effective. A second example shows the cost-benefit analysis methodology for
a distribution automation programme in case there is no predefined renewable energy target in Ruritania. This
exercise demonstrates a situation in which the benefits and costs of the additional renewable energy deployment
have to be considered as part of the analysis.
A third exercise is used to present a smart grid investment in an island country, and is loosely based on Jamaica.
This exercise focuses on a demand response (DR) project to accompany renewables expansion to defer
generation-capacity investment. The project is found to be cost-effective – even with moderate changes in
assumptions and with significant incentive payments to DR participants.
1
A fictional kingdom used as the setting for stories by Anthony Hope (1863-1933), and often used in academia to refer to a hypothetical country
3
Chapter 1
© LeahKat
Renewables, Smart Grids
and Cost-Benefit Analysis
Chapter summary: Smart grid projects must be evaluated and justified on an economic basis. The challenge for
decision-makers is to evaluate smart grid proposals for renewables rigorously, objectively, and with a well-defined
and consistent methodology. Developing countries present a clear opportunity for smart grids, including the
possibility of leapfrogging over outdated technologies and enhancing electricity access. There are challenges as
well, notably capital constraints and political challenges in setting electricity rates that cover costs.
R
enewable energy power generation is growing fast. Since 2011, more renewable power generation capacity
is added than conventional power generation capacity every single year. In particular, variable renewable
energy sources such as solar photovoltaics (PV) and wind are growing fast. 2014 was another record year
with around 44 GW of solar PV and 50 GW of wind power added globally.
IRENA’s global renewable energy roadmap, REmap 2030, suggests that the growth in renewables will continue
(IRENA, 2014). Based on an analysis of 26 countries, covering 75% of global energy consumption, the share of
renewables in the power sector may increase from 22% in 2012 to more than 40% by 2030, and the share of
variable renewables may increase from 3% in 2013 to around 20% in 2030.
Up to 2012, the growth of variable renewable energy took place in European countries and the United States.
However, in the last two years China and Japan have become the major markets for solar PV and wind power. Over
the next 20 years, it is expected that this shift continues, especially to those countries with growing electricity
demand in Asia, Latin America, and Africa. These countries are rapidly expanding their grid infrastructure to
keep up with the demand, and renewable power generation allows them to add capacity in a cost-effective and
timely manner.
4
S M A RT G RI DS A ND R E NE WA BL E S
Renewables are also a solution to improve the low
electrification rates in many developing countries.
Globally, as many as 1.2 billion people do not have
access to electricity and distributed power generation
of solar PV and wind could alleviate this situation.
However, this would require rapid expansion of
existing grids or the development of mini-grids with
decentralised control systems.
Achieving high shares of renewables in the final
energy mix can substantially benefit from electricity
systems that are more flexible, responsive, and
intelligent. “Smart grid technologies,” can do just that
by leveraging the tremendous technical advances
in information and computing. Hence, they are an
essential component of the REmap 2030 analysis
(IRENA 2014, p. 8).
Smart grids use technologies to instantly relay
information in order to match supply with demand,
support well-informed decisions on dispatch, and
keep systems operating at optimal efficiency. These
technologies can be implemented from utility-scale
generation to consumer appliances.
For example, just as a smart appliance in a private
home can switch on and off in response to varying
electricity prices, a smart transformer on the grid
can automatically notify grid operators and repair
personnel if its internal temperatures is too high.
Similarly, a smart meter can measure and track the
output of a rooftop photovoltaic (PV) system and
send that data to the utility, thereby making use of
surplus PV energy, or addressing gaps due to solar
variability.
There is no universal agreement on what qualifies
as a smart grid technology; however, it is generally
understood to include communication, information
management, and control technologies that contribute
to the efficiency and flexibility of an electricity
system’s operation. The suite of available smart grid
technologies and applications continues to evolve at
a rapid pace. Table 1A lists the seven major groups
of smart grid technologies and more details on these
technologies, including costs and market status, can
be found in the 2013 IRENA report on “Smart Grids
and Renewables” (IRENA, 2013). This list, however,
will continue to grow as entrepreneurs find novel
applications for improved intelligence and information
in the energy industry.
Table 1A: Smart Grid Technologies
Advanced metering infrastructure (AMI)
Advanced electricity pricing
Demand response (DR)
Distribution automation (DA)
Renewable resource forecasting
Smart inverters
Distributed storage
Virtual power plants
Microgrids
Smart grid technologies enable high levels of
renewables mainly by increasing grid flexibility and
facilitating the increased use of variable renewable
generation technologies, notably wind and PV
systems. However, smart grids also have profound
implications for transmission and distribution (T&D)
systems, as they can ease T&D system integration
of distributed renewable generation and reduce
T&D investment needs by optimising use of existing
infrastructure. This will become increasingly relevant
given that T&D is projected to account for almost half
of the power sector investment until 2035, much of
that in non-Organisation for Economic Co-operation
and Development (OECD) countries (International
Energy Agency [IEA], 2013).
IRENA’s Smart Grids and Renewables report explains
how smart grids enable renewables, discusses the
nontechnical barriers to smart grids, and details
the costs, performance, and other characteristics of
specific smart grid technologies. The report concluded
that smart grids, although conceptually attractive for
their ability to enable renewables, must be evaluated
and justified on an economic basis.
This report is IRENA’s second report on smart grids
and renewables. The aim of this report is to help
decision-makers in developing countries to perform
CBAs on smart grid projects. Such analyses are critical
for ensuring that scarce capital is invested wisely.
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
5
SMART GRID PROJECTS NEED
CAREFUL EVALUATION
SMART GRIDS IN THE DEVELOPING
WORLD
Numerous studies have concluded that smart grids can
be financially attractive investments. A meta-analysis
of 30 business cases for smart meter projects
in 12 countries, representing 4 continents, found
that on average, the net present value (NPV) of
project benefits exceeded the NPV of costs by
nearly two to one (King, 2012). In the Middle East
and North Africa, studies found that smart grid
investments could save the region USD 300 million to
USD 1 billion annually while helping to realise the
region’s potential for solar power (Northeast Group,
2012). A study in the U.S. found that potential
investments in sustainable technologies, including
smart grid and renewables, have an NPV of USD 20
billion to USD 25 billion based solely on benefits to
utilities (Rudden and Rudden, 2012). Although these
studies are based on predictions rather than actual
project results (hence, they should be interpreted
carefully), nevertheless, they suggest that smart grid
projects are economically viable options.
Several methodologies exist for economic evaluation
of smart grid projects. Among the most widely known
is one developed by the Electric Power Research
Institute (EPRI) (EPRI, 2010 and 2013) and modified
by the Joint Research Centre of the European
Commission (EC, 2012a and 2012b). This methodology
is rigorous and well documented, but it does require
further modification for use in the developing world.
A critical question facing utilities, governments,
and other decision-makers, however, is whether
their individual proposed smart grid project makes
financial sense. However, each individual project must
be assessed on its own merits.
The costs of smart grid projects are typically welldefined and straightforward to quantify. The benefits,
however, may not be. Some of the benefits, such as
decreased operations and maintenance (O&M) costs,
are relatively clear. Others such as improving consumer
information or enhancing grid resiliency clearly have
some value, but assigning a monetary value to these
types of benefits is quite challenging.
An additional challenge in evaluating smart grids is
that the benefits often flow to multiple stakeholders.
Improved consumer information is of some value to
consumers, but probably of less direct value to the
utility. Smart grid projects can enable higher levels of
renewables and thereby reduce carbon emissions, but
different stakeholders will value that carbon reduction
differently.
The challenge for decision-makers is to evaluate smart
grid proposals for renewables rigorously, objectively,
and with a well-defined and consistent methodology.
6
S M A RT G RI DS A ND R E NE WA BL E S
Developing countries’ electricity systems differ from
those industrialised countries in several ways. In some
cases, these differences create a clear opportunity for
smart grids:
•
In many cases, the electricity systems are
still expanding in order to reach residents
without grid access (Table 1B). This
provides an opportunity for “leapfrogging,”
as countries can take advantage of smart
grid technologies while building out the
T&D grid instead of having to retrofit the
existing infrastructure.
•
Smart grids enable a number of innovative
energy services that could help realise goals
of universal access to electricity, possible.
These include linking energy payments to
mobile phones, installing local charging
stations and building mini- and microgrids.
(Welsch et al. 2013).
•
Sometimes the electricity systems suffer
from relatively high levels of theft and
technical losses (Table 1B). Smart grids are
well suited to tracking and reducing these
types of losses by recording electricity load
across the lines.
•
On average, electricity systems have lower
reliability than those in industrialised
countries (Table 1C). As is the case for
theft and technical losses, smart grids
are particularly well suited to provide
significant improvements in reliability by
tracking any outages or faults in the lines.
There are ways in which these differences create a
clear challenge for smart grids:
•
•
•
•
Many (utilities in) developing countries
are capital-constrained, with limited
access to low-cost capital for upgrades
and extensions of their electricity systems.
This complicates efforts to invest in smart
grid projects, even if they are clearly costeffective.
grid project evaluation. One that can accommodate
the benefits such as reduced theft, grid extension,
increased reliability, and is simultaneously realistic
about system data availability and accuracy.
This report is the first to provide such a methodology
for developing countries.
The remainder of this report is organised as follows:
Electricity systems may be unable to
set electricity rates at a level that covers
costs of operation, due to concerns over
affordability of electricity for residents,
businesses, and industry. This leads to
insufficient O&M spending and a backlog
of basic maintenance, therefore making
it difficult to justify smart grid projects,
which may be seen as a luxury and/or not
absolutely critical to basic grid functioning.
Smart grid analyses may require detailed
data on system operational characteristics
(such
as
reliability/downtime
and
minute-by-minute load data), customer
demographics, and more. Such extensive
data may not be available.
•
Chapter 2 provides an overview of how
CBA works and what major issues need to
be considered before undertaking a CBA.
•
Chapter 3 provides a detailed guide on
how to estimate the benefits and costs of
a smart grid project with our methodology,
and how to use sensitivity analysis to
incorporate uncertainty and qualitative
benefits into a CBA-guided decision. The
text will be accompanied by an exercise
that will illustrate all of the different steps..
This is the most challenging component of
a CBA.
•
The appendices provide further details on
two additional case studies, and include
an illustration of the alternative approach
to be used when there is not an explicit
renewables goal. Furthermore, the case
study provides an explanation on how
to calculate the different benefits, and
provides starting-point cost data for any
analysis.
Regulatory and institutional issues,
such as the need for standard setting,
harmonisation of different electricity
systems, and ensuring data privacy, can
limit innovation.
Owing to these substantial differences, developing
countries require a customised methodology for smart
Table 1B: Electricity access and T&D losses
Region
Access to electricity
(% of population)
T&D losses (%)
Sub-Saharan Africa
(developing only)
35
12
Least-developed
countries: United Nations
classification
32
Middle income
85
11
>99*
7
European Union
16
Table 1C: Electricity outages in selected
countries
Country
Value lost due to electricity
outages (% of sales/year)
Hungary
1
Samoa
7
Yemen
13
Zimbabwe
18
Source: http://data.worldbank.org/indicator/IC.FRM.OUTG.ZS
Source: http://databank.worldbank.org/data/home.aspx
*Authors’ estimate
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
7
Chapter 2
© Francois Loubser
Cost-Benefit Analysis:
Introduction and Overview
Chapter summary: Before undertaking a CBA, one needs to consider several issues, including perspective (whose
costs and benefits are relevant) and baseline definition (what would happen in the absence of the proposed smart
grid project). There are two basic approaches to a smart grid CBA: one in which there is a predefined renewables
goal that will likely be met with or without the proposed smart grid project, and one in which the smart grid project
allows for renewables that wouldn’t otherwise occur. Our methodology works for both approaches.
COST-BENEFIT ANALYSIS IN CONTEXT
U
sed properly, CBA can give an estimate of how a smart grid technology investment will perform,
that is, the overall financial attractiveness of the investment. CBA’s principal strengths are:
•
It is relatively simple.
•
It is rigorous and quantitative, enabling evidence-based decision-making.
•
Its assumptions and methodology are transparent.
•
It lends itself to sensitivity analysis, allowing one to vary the value of uncertain inputs and see how
the results change.
However, there are some significant drawbacks as well, namely:
8
•
It is data-intensive. It requires quite a few inputs, some of which may not be known.
•
It requires an assessment of the future impacts of present-day investments, which is inherently
uncertain.
S M A RT G RI DS A ND R E NE WA BL E S
•
•
Specific (point value) inputs are required
and therefore, a sensitivity analysis may be
needed if the input values are uncertain.
Its incorporation of qualitative factors and
second-order impacts is imprecise.
CBA is an ideal first tool for evaluating a smart grid
investment. Its value lies not just in the result it
provides but also in how it requires one to define and
quantify the expected costs and benefits. It is this
analytical discipline, rather than the result itself, that
is often most informative.
CBA is one of several methods that can be used
to assess the economic impacts of a smart grid
investment. In the private sector, this assessment is
referred as the ‘business case’ and might relate to
the profit potential, competitive advantage, market
positioning, or other business attributes. However,
in most cases, electricity in developing countries
is provided by a government agency or a regulated
monopoly and therefore, competitive market issues
(such as market share) are not of concern. Instead, the
overall question is whether the benefits of investing in
smart grids are greater than its costs. This is a question
CBA can help answer.
CONSIDERATIONS BEFORE
UNDERTAKING A COST-BENEFIT
ANALYSIS
Several overarching issues need to be considered
before undertaking a CBA.
Costs and Benefits as Seen by Whom?
A smart grid project will have numerous benefits, all of
which will not be equally valued by the stakeholders.
For example, smart grid technologies might allow
for higher renewables and therefore lower carbon
emissions, but an individual electricity user may not
place any value on reducing carbon emissions. So
the CBA, if done from the individual electricity user’s
perspective, might value the higher renewables
benefit at 0. However, the utility may value those
emissions reductions quite highly (due to, for example,
a government commitment to reduce emissions), and
would therefore place a very different value on that
emissions reduction benefit.
Whose perspective does one take when doing
a CBA?
This report generally takes a societal perspective,
one that incorporates all costs and benefits as seen
by all stakeholders. In the carbon example above,
the utility’s perceived value of the carbon emissions
would be used. In most cases, smart grid projects in
developing countries are undertaken by government
agencies, and the perspective of these agencies is
typically close to that of society.
Note, however, that there is no requirement that
CBA take a societal perspective and a more narrow
perspective on costs and benefits can be taken. Where
possible, this handbook shows how costs and benefits
flow differently according to the stakeholders, allowing
users to tailor the analysis to different stakeholders
perspectives.
Importance of Qualitative Factors
Some costs and benefits of smart grid projects, such
as up-front hardware costs, are straightforward
to quantify. Others, such as allowing for increased
electricity system reliability, can be valued in monetary
terms, but with some uncertainty. Still others, such as
providing consumers with greater information and
control, certainly have some value, but are extremely
difficult to attach a monetary value to. We call these
qualitative benefits.
Our CBA methodology, detailed below, stresses the
importance of keeping track of all relevant costs
and benefits, including those not easily quantifiable.
However, in the end, decision-makers will need
to make a judgment on the relative value of the
qualitative benefits to the decision at hand. CBA can
clarify the uncertainties, but cannot eliminate them.
Second-Order Impacts
Imagine a smart grid project with an up-front hardware
cost of USD 10 million. From the utility’s perspective,
that USD 10 million is clearly a cost. However, from
the perspective of the company selling that hardware,
that is a USD 10 million benefit. USD 10 million that
may flow through the economy would have secondand higher-order benefits to the system as it does so.
CBA considers only the first-order impacts.
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
9
Need for a Baseline
Need to Select a Discount Rate
A CBA requires defining a baseline, or a prediction of
what would happen if the smart grid project were not
implemented. Firstly, that prediction is compared to
what is predicted to happen if the smart grid project
were implemented and secondly, the net benefits and
costs are calculated.
Most of the benefits associated with a smart grid
project occur in the future. A discount rate allows one
to find the present value of those future benefits—that
is, it allows one to unequivocally compare the future
benefits with the capital investments costs at the start
of the project. Doing a CBA of a smart grid project
requires one to select a discount rate. The selection of
a discount rate can significantly influence the results,
and it is therefore important to give careful thought as
to just what discount rate to use.
The CBA baseline is not necessarily a business-asusual or ”no changes” scenario. In fact, the baseline
should include all projects, or components of projects,
that are expected to take place with the exception
of the smart grid project that is under consideration.
For example, consider a utility undertaking a grid
extension project to serve new populations that do
not have current grid access.
The baseline would be grid extension, while the
smart grid analysis scenario is the grid extension with
smart grid technologies. Similarly, consider a utility
considering how smart grid technologies could assist
in reaching a goal of 30% renewables by 2020. That
goal would be part of the baseline, as it is predefined
and smart grid technologies will not alter that goal.
The CBA in this example would evaluate the costs and
benefits of implementing smart grid technologies, not
the costs and benefits of the goal itself.
If a CBA takes a societal perspective that incorporates
all costs and benefits accruable to society as a whole,
then a societal (sometimes called social) discount
rate should be used. The World Bank has adopted a
societal discount rate of 5% for certain kinds of debt
(IMF, 2003). However, a sensitivity analysis across
different different discount rates can be run to explore
the CBA results’ sensitivity to this assumption.
CBA Process Overview
At the highest and simplest level, CBA has three major
components:
•
Estimation of the benefits of the proposed
smart grid project. Our methodology,
illustrated in Chapter 3, focuses on the
benefits related to renewables integration;
however, the other potential benefits are
addressed as well.
•
Estimation of the costs of the proposed
smart grid project. This guide focuses
on up-front hardware costs and ongoing
operations and maintenance (O&M) costs.
O&M costs vary considerably according to
the project but can be roughly estimated
based on published data.
•
Comparison of costs and benefits. By
combining costs and benefits occurring at
different times through use of an overall
discount rate, an overall single number
(typically net present value, NPV) that can
be compared to other projects is obtained.
Need to Define Project Boundaries
The boundaries of a smart grid project need to be
clearly and carefully defined ahead of the CBA. In
general, the narrower the boundaries, the simpler
the analysis and the less uncertain the results. Critical
boundaries are determined by:
•
•
10
Time: the period of interest. Notably, for
what period the costs and benefits need to
be analyzed. For example, a project may
define this time frame as 10 years from
project launch.
Space: the geographic area of interest. This
is typically the utility service area or the
country as a whole.
S M A RT G RI DS A ND R E NE WA BL E S
However, this valuation is complicated by
the presence of qualitative benefits and by
uncertainty associated with the estimated
future cost and benefit values.
Two Fundamental Approaches
There are two fundamental approaches to smart grid
CBA for renewable implementation: (1) Start with an
explicit renewables deployment goal, to be met with
or without smart grid technologies (the Predefined
Renewables Goal approach); or (2) estimate the
renewables deployment that would be enabled by
the smart grid investment under consideration (the
No Predefined Renewables Goal approach). The
difference is essentially one of baseline, meaning
what one assumes would happen if the smart grid
investment were not made.
The Predefined Renewables Goal Approach
This approach is for situations in which there is a
preexisting renewables deployment goal, such as
“20% of electricity sold in 2020 must come from
renewable sources.” The smart grid investment under
consideration is one of perhaps several paths to reach
the goal.
In this case, the baseline is reaching the goal without
the smart grid investment. The CBA considers the
incremental costs and benefits—that is, those costs
and benefits resulting from the smart grid investment,
relative to the costs and benefits of the baseline.
Notably, in this approach the benefits of reaching set
RE goals, such as the associated carbon reduction,
are not included in the CBA, as these benefits are
assumed to occur in any case.
The No Predefined Renewables Goal Approach
This approach is for situations in which the smart grid
investment is seen as enabling the deployment of
renewables that otherwise would not occur. In this
case, the smart grid investment enables additional
renewables deployment, and the benefits and costs
associated with those renewables become part of the
CBA. The baseline is whatever renewables penetration
would occur in the absence of the smart grid project.
For ease of analysis, this is typically assumed to be
zero (or unchanged from the present renewables
level). This is more challenging since the amount of
renewables that the smart grid investment will enable
and the ensuing the costs and benefits, needs to be
estimated.
Costs = (smart grid project costs) plus
(enabled renewables costs)
Benefits = (smart grid project benefits) plus
(enabled renewables benefits)
Overview of Methodology
Our methodology for identifying and quantifying the
benefits of a smart grid is adapted from that proposed
by the Joint Research Centre EC (EC, 2012a and 2012b)
which was in turn adapted from EPRI (2010) and EPRI
(2013). Figure 2 shows an overview of this process.
After first identifying and defining the boundaries of
the project and the baseline against which it is to be
valued, the smart grid technologies to be deployed
are listed (Step 1). Each technology is then mapped
to the functions it provides (Step 2), and then each
function is in turn mapped to the benefits it provides
(Step 3). Finally, the economic value of each benefit
is monetised (Step 4). Costs are then estimated (Step
5), costs and benefits compared (Step 6), and finally a
sensitivity analysis is performed (Step 7).
The process is slightly more complicated if using
the No Predefined Renewables Goal approach to
incorporate the effect of renewables enabled by the
smart grid project, as shown on the right-hand side
of Figure 2. (As a reminder, under the No Predefined
Renewables Goal approach the new renewables are
treated as a benefit in the CBA; see Chapter 2 for
details). If enabling wind or solar is identified as a
potential benefit in Step 3, the enabled renewables
are mapped to their benefits and added to the list of
smart grid benefits. Then, the complete list of benefits
is monetised in Step 4.
This methodology can be complex—particularly in
estimating benefits (Steps 1 through 4 in Figure 2).
Chapter 3 provides a detailed case study on a fictional
country called Ruritania. Ruritania represents a small
developing country in which investments in smart
inverters are considered. More complex examples,
based on a distribution automation programme in
Ruritania and a demand response (DR) project in
Jamaica, can be found in Annex I and Annex II.
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
11
Figure 2
Yes
Start
v
No Predefined Renewables
Goal Approach
v
Predefined Renewables
Goal Approach
Step 1:
Define Project:
• Baseline
• List technologies
YES
(The New Reneables are treated
as a benefit in the CBA; see Annex I for details)
Does project
NO
facilitate a
predefined renewables
goal?
Step 1:
Define Project:
• Baseline
• List technologies
Step 2:
Map technologies
to function
Step 2:
Map technologies
to function
Step 3:
Map functions
to benefits
Step 3:
Map functions
to benefits
Step 4:
Monetize benefits
Step 4:
Monetize benefits
Step 5:
Quantify Costs
Step 5:
Quantify Costs
Step 6:
Compare costs
and benefits
Step 6:
Compare costs
and benefits
Step 7:
Perform sensitivity
analysis
Step 7:
Perform sensitivity
analysis
Enables
renewables?
NO
12
S M A RT G RI DS A ND R E NE WA BL E S
End
YES
Map renewable
functions to
benefits
© gui jun peng
Chapter 3
© pedrosala
Smart Grid Cost-Benefit
Analysis for Ruritania2
Chapter summary: The CBA methodology can be a bit complex. To demonstrate its use, it is first applied to a
simplified, fictional country called Ruritania. The exercise assesses the impact of smart inverters and reveals that
fewer outages and reduced losses are by far the most valuable benefits of these inverters, accounting for almost
three-fourths of the total benefit value. The advantages of smart inverters clearly exceed the costs. This result,
however, reflects electricity users’ estimated valuation of reduced outages. If the utility values fewer outages only
at the value of the lost electricity sales, then the smart inverters are not cost-effective.
The exercise illustrates how sensitivity analysis reveals the way in which net benefits vary with critical assumptions.
RURITANIA CASE STUDY SUMMARY
R
uritania is a country with an electricity system primarily based on coal- and gas-fired power stations, and
only 2% of variable renewables (wind). Its electricity system is expected to growth from 10 GW peak to
20 GW peak by 2030. Ruritania has a goal of 20% renewable electricity by 2030, to be met with wind
and solar PV. The costs and benefits of upgrading the planned wind and PV systems to include advanced grid
support features are analysed. The case study assumes that the renewable energy goal of 20% will not change,
so the CBA will only consider the costs and benefits on the electricity system.
The CBA looks specifically at the advanced grid support features. It assumes an upgrade of new wind and PV
with smart inverters. The smart inverters will provide grid-friendly features like fault ride-through and assistance
with voltage and frequency regulation. The smart inverters will be rolled out alongside the deployment of the
renewables to achieve the 20% renewable energy target assuming a project rollout time of 15 years. The CBA will
apply to this 15-year period.
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S M A RT G RI DS A ND R E NE WA BL E S
Benefits
The functions (in a smart grid CBA) of the gridfriendly controls are to enable wide-area monitoring
and visualisation, power flow control, and automated
voltage and volt-ampere reactive (VAR) control. These
functions map to 13 out of the 24 possible benefits, as
shown in Table 3A3. The full list of possible benefits
are discussed in Table 3D, and Table 3F will show how
these benefits were calculated.
One qualitative benefit of this project is to provide
utility workers with experience in advanced
technologies for control of renewables. The smart
PV inverters could also be integrated into future DA
schemes, acting as distributed reactive power sources
for voltage optimisation and thus, resulting in further
loss reductions and investment deferral.
2
A fictional kingdom used as the setting for stories by Anthony Hope
(1863-1933), and often used in academia to refer to a hypothetical
country
3
Only those benefits relevant to this project are listed. Additional
benefits may be relevant to a smart grid project, depending on the
project specifics.
Table 3A: Values of benefits
Benefit
NPV (thousand USD)
Uncertainty level
Primary beneficiary
Reduced ancillary service cost
4 300
Medium
Utility
Deferred distribution investments
2 300
Medium
Utility
Reduced equipment failures
8
Medium
Utility
Reduced distribution operations cost
0
Low
Utility
Medium
Utility
0
Low
Customers
29 000
High
Customers
Reduced major outages
0
High
Customers
Reduced restoration cost
70
High
Utility
Reduced momentary outages
0
Low
Customers
Reduced sags and swells
0
Medium
Customers
9 600
Medium
Society
Reduced SOx, NOx , and PM10
emissions
1 100
Medium
Society
Reduced wide-scale blackouts
0
High
Society
Reduced electricity losses
Reduced electricity cost
Reduced sustained outages
Reduced CO2 emissions
17 600
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
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Costs
The costs relevant to the CBA are any costs that
would not be incurred if the grid-friendly renewable
controls were not used. For PV systems, an average
inverter cost increase due to these grid-friendly
controls of 10% is assumed. For wind plants, the total
system capital cost is assumed to rise by 1%. In both
cases, these costs include the communications and IT
infrastructure required. Project costs are summarised
in Table 3B.
Discussion
The total present value of the project benefits is USD 63
million, while the total present value of the costs is
USD 38 million. The benefits exceed the costs, so the
project is cost-effective.
in Table 3A assume a value of USD 3 for each kWh
reduction in sustained losses. That value is based on
a meta-analysis of a large number of studies. Note,
however, that this USD 3/kWh value reflects the
electricity user’s perspective. The utility, in contrast,
might value these reduced outages as worth only the
regained electricity sales they yield, at the current
retail rate of electricity.
Changing the value of these outage reductions from
USD 3/kWh to USD 0.10/kWh changes the NPV from
plus USD 25 million to minus USD 2 million. In this case,
the project would no longer be cost-effective based
on quantified benefits alone; the qualitative benefits
would need to be worth at least USD 2 million to make
the project cost-effective under this reduced-benefit
scenario.
As shown in Table 3A, reduced sustained outages and
reduced electricity losses are the largest components
of the benefits NPV. A critical assumption in estimating
the benefit of reduced sustained outages is the value
of this reduction to electricity users. The results
Table 3B: Values of costs
Cost
16
NPV (thousand USD)
Uncertainty level
Primary beneficiary
Advanced PV inverters
23 800
Low
Utility and PV
owners
Advanced wind turbines
14 200
Medium
Utility and wind
owners
S M A RT G RI DS A ND R E NE WA BL E S
STEP 1: DEFINE PROJECT
The first step in analyzing the costs and benefits of a
project is to clearly define the project. This includes
recording general project information, identifying the
technologies being deployed, and determining the
baseline for the CBA.
Box 1 shows the project definition for Ruritania.
In this chapter, we illustrate the CBA methodology
using a fictional case study: the Ruritania grid
support project. The details are described in sidebars
throughout this section.
Relevant general project information may include the
goals of the project, its stakeholders, its regulatory
environment and its dimensions and boundaries. One
key dimension is the length of the project, as it will
define the window within which costs and benefits
should be included in the CBA.
Box 1: Ruritania Smart Grid Project: Country Information
The Ruritania example is based on a hypothetical power system and shows how the methodology might apply
to that system. The intent is to provide an example of each step rather than to show a complete CBA, which
would include more detail.
General country information:
•
10 GW peak electricity demand, growing to 20 GW in 2030 (4.7% annual growth)
•
31 TWh annual generation, growing to 62 TWh in 2030
•
1 000 distribution feeders, doubling to 2 000 by 2030; average feeder capacity of 10 MW
•
Renewables goal: 20% of electricity from wind and solar by 2030 (currently at 2%); 70% of renewable
energy to come from wind plants, 15% from centralised PV plants, and 15% from distributed PV
installations
•
The capacity factor for wind is taken to be 40%, and the capacity factor for PV is taken to be 21%,
based on median data from OpenEI
•
Electric utility is owned and managed by the national government
•
Average retail electricity price: USD 0.10 per kWh
•
Average wholesale electricity price: USD 0.05 per kWh
•
Annual discount rate used for government planning: 8%
•
Annual price inflation rate: 3%
Proposed smart grid project:
•
Goal: facilitate the preexisting renewable deployment goal
•
Project term: 15 years (2015 to 2030)
•
Project scope: nationwide
•
Stakeholders: utility, electricity consumers, society at large
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
17
List Technologies
After compiling general project information, the
specific smart grid technologies and renewable
assets under consideration should be listed. IRENA’s
publication ‘Smart Grids and Renewables’ provides an
overview of six categories of smart grid technologies
and applications that can be considered as “wellestablished” or “advanced.” Energy storage and
microgrids should also be considered as potential
technology options, because they may find niche
applications in emerging countries (IRENA, 2015).
Annex IV provides a summary of these six categories
of smart grid options. Box 2 illustrates the smart grid
technology chosen in our case study.
Box 2: Ruritania Smart Grid Project: Introducing Smart Controls
In our illustrative example project, we propose to upgrade the wind turbines and solar
inverters to include advanced grid support capability that uses improved controls to help
renewables integrate with the grid, assisting with voltage and/or frequency regulation
and remaining connected during grid faults. We will refer to this upgrade as grid-friendly
renewable controls.
Select Baseline
If an RE goal has been set prior to the smart grid
project (the Predefined Renewables Goal approach),
then the baseline for the CBA involves achieving that
goal without using smart grid technologies (or without
adding smart grid assets if some already exist).
Other sources of the flexibility needed to integrate
renewables include upgrades to grid infrastructure and
investment in more-flexible conventional generators.
IEA provides guidance as to amounts of flexibility
available from various conventional generators and
infrastructure upgrades as well as economic analyses
of the different options (IEA, 2014).
When no prior RE goal exists (the No Predefined
Renewables Goal approach), the baseline involves
continuing with existing grid maintenance and
development plans.
Under either approach, the baseline should be carefully
chosen to include projected future changes to the
electricity system. For instance, if there is a preexisting
goal to expand electricity access to unserved areas or
to greatly increase the hours of electricity availability,
then the baseline for the CBA involves achieving that
goal without the proposed smart grid technologies.
In this case, the smart grid project may have the
opportunity to leapfrog conventional electrification
technologies. Box 3 illustrates the baseline selection
for our case study.
Box 3: Ruritania Smart Grid Project: Baseline Selection
Because the smart grid project under consideration facilitates an existing renewables goal,
the Predefined Renewables Goal approach will be used. Therefore, the baseline against
which the project will be compared involves achieving the renewables goal without using
the proposed smart grid technology. Long-term power system modelling should be used
to determine which conventional technologies would be needed to facilitate the 20%
renewables goal. For the sake of this example, we assume that the country has determined
that in order to meet its goal without smart grid technologies, it will need to install 3 GW
of combined-cycle gas-fired turbines designed for flexible operation, retrofit 3 GW of
existing coal-fired generation for improved flexibility, install 300 kilometers of additional
transmission lines with a peak capacity of 2 GW, and install additional switched capacitor
banks on 10% of existing and new distribution circuits. As detailed later in this chapter,
advanced grid support from renewables will reduce the costs associated with some (but
not all) of these upgrades. The baseline includes conventional solar and wind inverters (not
capable of volt-VAR control or ride-through).
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S M A RT G RI DS A ND R E NE WA BL E S
STEP 2: MAP TECHNOLOGIES TO
FUNCTIONS
Once the technologies under consideration and the
baseline have been identified, the next step is to map
each technology to its potential functions. Functions,
in a smart grid sense, are the roles that various
technologies can play in improving grid operation.
Functions do not translate directly into monetary
values but are monetised in the next step by mapping
them to benefits. Thinking through the functions of
the smart grid technologies being deployed (rather
than trying to jump directly from technologies to
benefits) helps ensure a thorough analysis that does
not miss any benefits.
More than one example of a given technology
category may be included in the matrix. For example,
one project might include both direct utility control
of industrial loads and optimised control of residential
water heaters, both of which are examples of DR. Each
should receive its own column in the matrix.
If in doubt as to whether a given technology may
activate a certain function, include the function for
later consideration. Any technology may activate
more than one function, and any function may be
activated by more than one technology. In addition,
users of this CBA method may consider adding other
functions as appropriate.
In total, there are 12 functions (EPRI, 2010), shown.
Choosing from the list, consider which functions each
technology may activate and create a matrix like the
example for our case study shown in Table 3C.
Box 4: Ruritania Smart Grid Project: Mapping Technologies to Functions
Table 3C shows a matrix mapping the smart grid technology installed in our example
project to its functions. The smart grid technology under consideration is listed across
the top row. The first column contains all possible functions. Checkmarks indicate where a
given technology may provide a certain function.
Table 3C: Mapping technologies to function
Functions
Technology: Smart PV Inverters
Technology: Wind turbines with
advanced grid support function
Fault current limiting
Wide-area monitoring and visualisation






Dynamic capability rating
Flow control
Adaptive protection
Automated feeder switching
Automated voltage and VAR control
Diagnosis and notification of equipment
condition
Enhanced fault protection
Real-time load measurement and management
Real-time load transfer
Customer electricity-use optimisation
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
19
STEP 3: MAP FUNCTIONS TO BENEFITS
After mapping technologies to functions, each function
is in turn mapped to the benefits it may provide. A
benefit is any impact of the project that may have value
to any stakeholder (for example, utility, customer, or
society), following EPRI (EPRI, 2010). If using the No
Predefined Renewables Goal approach, the potential
list of benefits includes enabled renewables (wind and
solar). Again, use a matrix to ensure that each function
is considered in conjunction with each benefit. Include
all benefits that may possibly be activated; the next
step will determine what economic value each benefit
has, if any.
If using the No Predefined Renewables Goal approach,
if any of the functions may enable wind or solar,
the benefits of the enabled renewables should be
considered as well. In this situation, a new table for the
enabled wind and/or solar is needed, and the enabled
renewables are mapped to any additional benefits
they enable (such as pollutant reductions and/or fuel
cost reductions). This additional step appears as a box
on the far right in Figure 2.
Box 5: Ruritania Smart Grid Project: Mapping Functions to Benefits
Table 3D shows how the functions identified in Step 2 are mapped onto the benefits for the
case of Ruritania. Note that while this case study only involved one smart grid technology,
most eligible benefits received at least one checkmark in Table 3D. However, this does not
mean that all checked benefits will have associated value when monetised in the next step.
Because the Ruritania case study uses the Predefined Renewables Goal approach, enabled
wind and solar generation are not applicable benefits in the CBA and hence are not
evaluated in Table 3D.
STEP 4: MONETISE BENEFITS
Once a comprehensive list of potential benefits is
generated, the next step is to monetise each benefit
by estimating its value in monetary terms. The value
of each benefit should be considered relative to the
baseline case; often the benefit will take the form of a
cost savings relative to the baseline.
project each benefit accrues, as this information will
be needed to discount all future benefits to their
present-day values. The value of each benefit should
be estimated for each year of the project. A detailed
description of the different benefits, and methods to
evaluate their value is provided in Annex III.
This step is the crux of the CBA and will likely be the
most difficult. Estimating the value of some benefits
may require power system modelling and simulation,
which in turn requires detailed data on the power
system and projections of the future state of the
system in the baseline case and with the smart grid
project.
Uncertainty in Benefit Values
While monetising the benefits, determine which
stakeholders receive the benefits so that the net cost
or benefit to each stakeholder can be estimated.
Stakeholder groups will typically include grid
operators, consumers, and society at large.
It is also important to determine when during the
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S M A RT G RI DS A ND R E NE WA BL E S
There will inevitably be some degree of uncertainty
in all benefit values. Estimate the magnitude of
uncertainty of each benefit using the four-level scale
given in EPRI (EPRI, 2010) as shown in Table 3E. Values
with high uncertainty may be good candidates for
sensitivity analysis. In addition, uncertainty estimates
will allow decision-makers to gauge the overall level of
certainty of the CBA.
Table 3D: Mapping functions to benefits
Benefits
Function: Wide-area
monitoring & visualisation
Function: Flow
control
Function: Automated
voltage & VAR control
Optimised generator
operation
Reduced generation
capacity investments
Reduced ancillary service
cost

Reduced congestion cost
Deferred transmission
capacity investments
Deferred distribution
investments

Reduced equipment
failures








Reduced distribution
equipment maintenance
cost
Reduced distribution
operations cost
Reduced meter reading
cost
Reduced electricity theft
Reduced electricity losses
Reduced electricity cost
Reduced sustained outages

Reduced major outages

Reduced restoration cost

Reduced momentary
outages


Reduced sags and swells

Reduced CO2 emissions

Reduced SOx, NOx, and
PM10 emissions

Reduced fuel costs
Reduced wide-scale
blackouts


Enabled wind generation
NA
NA
NA
Enabled solar generation
NA
NA
NA
NA = not applicable, as these benefits are relevant only for the No Predefined Renewables Goal Approach. See Annex I for details.
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
21
Table 3E: Categories of uncertainty levels
Level of uncertainty
Explanation
Low
A low level of uncertainty in quantitative estimates and/or in monetisation implies a level of precision
where the estimate is viewed to be accurate +/- 20%, with at least an 80% level of confidence, i.e.,
there is an 80% probability that the actual value is within +/- 20% of the estimate.
Medium
This category is for estimates viewed to be accurate +/- 40%, with at least an 80% level of confidence,
i.e., there is an 80% probability that the actual value is within +/- 40% of the estimate.
High
This category is for estimates that are very uncertain and difficult to quantify. The precision level is
viewed as +/- 100%, with a 95% level of confidence.
Cannot be quantified
This assignment should be limited to benefits that fall into the speculative category and are so
uncertain that they can only be expressed as an order-of-magnitude estimate.
Adapted from EPRI, 2010, as reproduced in Giordano et al., 2012a and 2012b
Box 6: Ruritania Smart Grid Project: Monetising Benefits
Table 3F shows the estimated value of each benefit for the case of Ruritania, along with
brief notes on how the value was estimated and the uncertainty level of the estimate.
For this simple example, many assumptions were made on monetisation input values for
illustrative purposes. A full CBA would incorporate more-detailed modelling and forecasting
using the best data available to produce the inputs needed to monetise benefits. For
example, our assumption that the use of smart PV inverters with volt-VAR control will avoid
the need for 200 switched capacitor banks could be confirmed by modelling and simulation
of typical distribution feeders in the region using IRENA’s grid stability methodology.
All of the benefits have zero value in the first year because the first grid-friendly turbines
and inverters are installed that year. The benefit values increase gradually each year as
more installations occur, reaching a maximum annual value in year 15.
The largest benefit comes from reduced sustained outages at USD 19 million in year 15, but
note that the uncertainty of this benefit is high. The next largest benefit is from reduced
electricity losses, coming in at USD 17 million during year 15. Several of the possible benefits
checked in Table 3D turn out to have no quantifiable value in this project; this is to be
expected.
Qualitative Benefits
In additional to monetisable impacts, smart grid
projects also produce benefits that are more difficult
to quantify. These may include improvement of local
workforce capabilities, improved safety, greater
inclusion of consumers, and other benefits (Giordano
et al., 2012a and 2012b). While the availability of
skilled workers may present a challenge in developing
countries, building skills in the local workforce may be
an especially important benefit for the same reason.
When smart grid projects are used to provide
electricity to previously unserved (or underserved)
areas, a number of significant benefits may come into
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S M A RT G RI DS A ND R E NE WA BL E S
play that are difficult to put a price on. These include
improved health (for example, fewer health issues due
to reduced burning of wood, charcoal and kerosene)
and improved access to healthcare services and
health clinics. Educational benefits due to improved
school conditions and the ability to study after dark
can also be significant. Entrepreneurial opportunities
can also bring significant benefits, allowing people to
better provide for their own needs and those of their
communities (World Bank, 2014). It may be possible
to put a rough quantitative value on the economic
benefits of electrification based on various studies by
the World Bank and others. However, when deciding
whether these benefits are attributable to a smart
grid project, it is important to consider the baseline:
Often the most appropriate baseline is not lack of
electrification but electrification using conventional
technologies, in which case the smart grid project
would not get credit for the benefits of electrification
since they also exist in the baseline case.
When smart grids help reduce electricity theft, this
provides a quantitative benefit to the utility (and
indirectly to its paying customers), but the loss of
access by people who had been stealing electricity
is a dis-benefit or cost to those people. This cost
may be hard to quantify, but it is worth considering
that reduced theft may create a need for subsidised
electricity. This qualitative cost will slightly reduce the
quantified benefit.
Smart grid technologies tend to be mutually
reinforcing, so a significant benefit of a smart grid
project is that it provides a basis for future smart grid
projects to build upon. For example, if a DA system
and associated measurement and control hardware
are first installed with a goal of speeding recovery
following electrical faults, that same system could
later be used for other tasks such as optimizing system
voltage to reduce losses (see the exercise in Annex
I). The financial payback of the second project will
be greatly improved because it uses already existing
assets.
Smart grid projects may also enable future renewable
energy deployment that is not currently under
consideration. Under the Predefined Renewables Goal
approach, this characterisation would be renewable
deployments that go beyond the preset goal. Under
the No Predefined Renewables Goal approach, this
characterisation means renewable energy beyond
the amount assumed, to be enabled by the smart grid
project under the quantitative CBA.
While the benefits considered in this section are difficult
to monetise, it is important to try to quantify them
to the extent possible to allow for comparison with
other projects. For instance, if a project is expected to
develop workforce skills, state specifically what skills
are expected to be gained and approximately how
many people will gain those skills. A detailed method
for quantifying benefits that cannot be monetised is
provided by the Joint Research Commission (EC, 2012a
and 2012b). In Annex III of this report, guidelines are
given for applying weighting factors to nonmonetised
benefits so that they may be included in the integrated
CBA.
Note that the functions and benefits of smart grid
technologies are highly interdependent. Hence, the
CBA method presented here risks missing some
synergistic benefits (or double-counting benefits that
are attributed to multiple technologies). System-level
analysis can better manage these synergies, but there
are few well-developed methodologies for CBA at a
system level.4
4
This cautionary note was prompted by Chapter 4 of (IEA, 2014),
which similarly cautions against missing system-level effects when
tallying individual renewable integration costs.
Box 7: Ruritania Smart Grid Project: Assessing Qualitative Benefits
The qualitative benefits of the example smart grid project include giving utility workers
experience with advanced renewables control technologies. These skills will be transferable
to future renewable energy and smart grid projects. The smart PV inverters could also be
integrated into future DA schemes, acting as distributed reactive power sources for voltage
optimisation, resulting in further loss reductions and investment deferral.
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
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Table 3F: Monetised benefits of the example system
Benefit
NPV
Uncertainty level
Primary beneficiary
Reduced
ancillary service
cost
USD 0 in year 1,
ramping to USD 3.6M
in year 15
Renewables’ ability to ride through voltage and Medium
frequency events (thus not exacerbating those
events) is assumed to reduce frequency regulation
needs by 5%. High-frequency power curtailment
from wind, PV, and virtual inertia from wind are
assumed to reduce frequency regulation costs by
an additional 5%. This benefit starts from zero and
ramps to a total savings of 10% over the project life.
The demand for frequency regulation is assumed to
average 0.65% of load demand based on data from
a U.S. transmission operator (Monitoring Analytics,
2013), and the cost of regulation is assumed to be
USD 20/MWh, increasing to USD 31/MWh by year 15
due to inflation. Year-15 savings = (20 GW demand)
* (0.0065) * (8760 hours/year) * (USD 31/MWh
regulation) * (0.10) = USD 3.6M.
Deferred
distribution
investments
USD 0 in year 1,
ramping to USD 1.4M
in year 15
Smart distributed PV inverters performing volt- Medium
VAR control are assumed to avoid the need for 200
switched capacitor banks (a conventional source of
voltage control) rated at 500 kVAR each, costing
USD 13/kVAR (Eaton, 2014) plus USD 1 000 per bank
to install. With inflation, this benefit comes to USD
410K in year 15.
We also assume that 50% of the distributed PV
systems are targeted at capacity-constrained
distribution feeders, where the peak load coincides
well with PV output, providing a benefit of USD
0.001/kWh of PV in deferred distribution investment,
in the lower half of the range identified in (Beck,
2009). This benefit comes to USD 940K in year 15
24
Reduced
equipment
failures
USD 0 in year 1,
Inverter-based volt-VAR control is assumed to Medium
ramping to USD 5 100 reduce annual equipment failures by 10% once the
in year 15
project is complete, due to reduced switching of
capacitor banks, which are assumed to be on 15% of
existing feeders for a total of 150 banks. The baseline
annual failure rate is assumed to be 3%. This benefit
ramps from zero to its full value over the project
life. Year-15 savings: (150 capacitor banks) * (0.03
baseline failure rate) * (0.1 reduction in failure rate) *
(USD 11 000/bank after inflation) = USD 4 950
Reduced
distribution
operations cost
USD 0
S M A RT G RI DS A ND R E NE WA BL E S
While inverter-based volt-VAR control could Low
result in a reduction in manual capacitor switching
operations, this benefit is assumed to have negligible
value for this project.
Reduced
USD 0 in year 1,
electricity losses ramping to USD 17M
in year 15
By providing distributed sources of reactive power, Medium
smart distributed PV inverters are expected to
reduce distribution line losses by 5% once all are
installed. Total distribution losses are assumed to
be 7% of the energy delivered. The USD 50/MWh
wholesale electricity cost escalates to USD 78/MWh
by year 15 given 3% inflation. Year-15 loss reduction
= (62 GWh demand) * (0.07 loss rate) * (0.05 loss
reduction) = 217 GWh. Year-15 savings: (218 000
MWh) * (USD 78/MWh) = USD 17M
Reduced
electricity cost
USD 0
While it is possible that the use of grid-friendly Low
controls could lead to a reduction in electricity cost,
no such reduction is assumed here.
Reduced
sustained
outages
USD 0 in year 1,
ramping to USD 19M
in year 15
In this example, by riding through voltage and High
frequency events and some momentary outages,
and by contributing to voltage and frequency
regulation, grid-friendly controls are assumed to
reduce sustained outages by 1% once fully installed.
An average VOLL of USD 3/kWh and an average
load per customer of 1 kW are assumed. By year 15,
the VOLL will be USD 4.70/kWh due to inflation.
The total annual number of outages per customer is
estimated by multiplying SAIFI by SAIDI, assuming a
SAIFI of 10 outages per year and a SAIDI of 2 hours
per outage. Hence the year-15 baseline outage cost
is (10 outages/year/customer) * (2 hours/outage)
* (USD 4.7/kWh) * (1 kW/customer) = USD 94 per
customer. With 20 million customers in year 15, the
total value of this benefit that year is (20 million) *
(USD 94) * (0.01) = USD 19M.
Reduced major
outages
USD 0
No reduction in major outages is assumed for this High
project.
Reduced
restoration cost
USD 0 in year 1,
ramping to USD 47K
in year 15
We assume each feeder has 10 outages per year High
that require manual restoration, and that restoration
costs USD 150 in crew time (or USD 235 in year 15).
The 1% reduction in outages mentioned above then
results in a year-15 savings of (USD 235/outage) *
(10 outages/year/feeder) * (2 000 feeders) * (0.01
reduction) = USD 47 000.
Reduced
momentary
outages
USD 0
No reduction in momentary outages is assumed for Low
this project.
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
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26
Reduced sags
and swells
USD 0
Grid-friendly renewables can inject real and reactive Medium
power into the grid during voltage sags to reduce
the magnitude of the event. However, because
reductions in such events are of quantifiable value
only to customers with sensitive loads, and because
the effect is very system-specific, we do not assume
any value for this benefit.
Reduced CO2
emissions
USD 0 in year 1,
ramping to USD 9.3M
in year 15
Because the solar and wind plants themselves are Medium
present in the baseline case, their CO2 reduction
is not a benefit of the project. However, CO2
reductions from decreased distribution losses due
to local reactive power provision by smart inverters
are a benefit of the project. Each MWh saved is
assumed to save 0.68 tons of CO2 based on a mix
of coal, gas, hydroelectric, and renewable sources.
Assuming a social cost of carbon of USD 40/ton CO2
(inflated to USD 63/ton by year 15) and following the
assumptions in the Reduced Electricity Losses row
above, the year-15 benefit is (218 000 MWh) * (0.68
tons CO2/MWh) * (USD 63/ton CO2) = USD 9.3M.
SOx, NOx, and
PM10 emissions
SOx: USD 0 in year 1,
ramping to
USD 800K in year 15;
NOx: USD 0 in year 1,
ramping to USD 110K
in year 15; PM10:
USD 0 in year 1,
USD 10K in year 15
Reduced distribution losses due to local reactive Medium
power provision also result in reduced SOx, NOx,
and PM10 emissions. We assume that each MWh
produced from coal emits 5 kg SO2, 3 kg NOx, and
1 kg PM10 on average. We also assume that 30%
of the country’s electricity comes from coal at the
beginning of the project, and that the amount of
energy from coal remains constant throughout the
project at 9.2 GWh/year. The social costs of each
pollutant are assumed to be half of the U.S. market
values, or USD 3.15/kg SOx, USD 0.7/kg NOx, and
USD 0.2/kg PM10, and are adjusted for inflation. The
year-15 value of SOx reductions is (218 000 MWh
loss reduction) * (0.15 portion from coal) * (5 kg SOx/
MWh) * (USD 4.94/kg SOx) = USD 800K. Similarly,
the year-15 values of NOx and PM10 reductions are
USD 110K and USD 10K, respectively.
Reduced widescale blackouts
USD 0
By riding through voltage and frequency events High
and some momentary outages, and by contributing
to voltage and frequency regulation, grid-friendly
controls can reduce the likelihood of a large blackout.
However, we do not assume any quantifiable benefit
in this example.
S M A RT G RI DS A ND R E NE WA BL E S
STEP 5: QUANTIFY COSTS
Cost Categories
The first steps differentiate between the costs
associated with the baseline, and the costs associated
with the smart grid project. The baseline should include
all costs associated with operating the electricity
system. Costs attributed to the project, in contrast,
should be only those that the project imposes or
adds. Costs are by definition negative, or outlays; cost
savings or reductions should be tracked as benefits.
Costs can be divided into four categories:
•
Up-front hardware costs. Also called
capital or initial costs, these expenses are
one-time costs associated with purchasing
the specific technologies.
•
Project implementation costs. These costs
are associated with installation, marketing,
scheduling,
project
management,
commissioning, and other cost components
of project implementation.
•
Operating and maintenance costs. These
expenses are ongoing, with typical units of
USD/year or USD/MWh.
•
Qualitative costs. These various expenses
are difficult to quantify yet still relevant to
the analysis.
As with all cost estimations, there are uncertainties,
notably:
•
Cost overruns, due to lack of field experience
with new smart grid technologies.
•
Unexpected marketing costs for those
technologies
requiring
consumer
participation.
•
Integration of new technologies into
existing grid systems, involving multiple
vendors.
In general, however, these cost uncertainties will be
smaller than the benefit-side uncertainties.
Typical capital and O&M costs are shown in Table 3G;
however, costs are very project-dependent and actual,
firm quotes from vendors should be used whenever
possible. As discussed in Chapter 2, if there is no
Table 3G: Categories of uncertainty levels
Technology
Typical capital costs
Typical O&M costs
Advanced metering USD 100–150/meter (meter only); USD 200– USD 0.5–1/meter/month
infrastructure
250/meter including communications and IT
systems
Advanced electricity Varies; typically low if AMI already installed
pricing
Varies; typically low
Demand response
Varies; USD 2–5/kW/year
USD 100–250/kW capacity
Distribution automa- Depends on specific technology and installation Depends on specific technology and installation
tion
Renewable resource None (typically purchased as a service)
forecasting
Wind forecasting service USD 2 500/month/plant;
PV expected to be similar
Smart inverters
< 5% more than conventional inverter; < 1% Same as conventional technologies
more than conventional wind turbine
Energy storage
Li-ion: USD 500–1 000/kWh; pumped hydro: 1%–2% of capital costs per year
USD 500–4 000/kW plus USD 0–200/kWh
Sources: Asmus, 2010; Idaho Power Company, 2013; IEA, 2014; IRENA 2013; Ontario Energy Board, 2011; U.S DOE, 2006; authors’ estimates
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
27
explicit renewables goal, then the new renewables
that a smart grid project make feasible do not fall
under the baseline. In this situation, the costs of the
new renewables need to be included in the CBA.
Extensive data on the costs of renewable electricity
generating technologies are available at http://costing.
irena.org. Typical costs are summarised in Table 3H.
Project implementation costs are very projectdependent. In general, bids from suppliers should
specify which specific project implementation steps
are included—for example, is the bid for just delivery
of a technology, or does it include installation, testing
and commissioning, and/or monitoring? Project
management costs should be considered as well,
particularly if a technology is new and/or will require
adding personnel. In general, technologies that are
new and/or unfamiliar to an organisation will have
higher implementation costs, as new processes and
organisational learning will be needed.
Qualitative costs can include management time and
attention, risks associated with going over budget or
with technical underperformance, and other similar
factors that are difficult to assign a monetary value
yet are clearly non-zero and need consideration. In
some cases, these costs can fall on electricity users.
For example, a smart grid project that enables
commercial building load reduction may cycle airconditioning compressors, thereby have a cost of
decreased building occupant comfort.
It is simplest to assume that costs all occur at the
beginning of the project. However, it may be more
accurate to recognise that some costs—particularly
project implementation and some types of qualitative
costs—can occur throughout the project life. If that is
the case, it is necessary to discount those costs and
calculate an NPV of the costs.
Table 3H: Typical renewable energy capital and O&M costs
Technology
Typical capital costs (USD/kW)
Typical O&M costs (USD/kW/yr)
Wind power plant
(utility-scale)
1 280-2 200
20-40
PV power
(utility-scale)
1 300-2 600
25
1 600-5 000
25
plant
Distributed PV
Sources: IRENA ,2015a; International Renewable Energy Agency Renewable Cost Database.
Box 8: Ruritania Smart Grid Project: Quantifying Costs
Our example smart grid project involves upgrading wind turbines and solar PV inverters
to provide advanced grid support. The first costs for these upgrades are typically given as a
percentage increase in the first costs for these inverters. Advanced wind turbines are penetrating
the market rapidly, and we assume that this option increases wind turbine first costs by 1%. For
PV, we assume that this upgrade increases the cost of the inverter (not the system) by 10%.
We also assume that there are no additional O&M costs for these upgrades. There are costs
associated with operating the wind turbines and PV systems, of course; however, these costs are
incurred regardless and thus are defined as being in the baseline.
Similarly, we assume that there are no additional project implementation costs. These
technologies are commercially available and require little if any additional effort beyond the
baseline technology.
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S M A RT G RI DS A ND R E NE WA BL E S
STEP 6: COMPARE COSTS AND
BENEFITS
The benefits of a proposed smart grid project, as
discussed in Chapter 2, occur over the lifetime of
the project. The costs, in contrast, typically involve a
large outlay at the beginning of the project, possibly
followed by a much smaller annual spending for O&M.
So, how can the different costs and benefits occurring
at different times be compared and assessed?
Fortunately, there are several financial analysis tools
for such a problem.
Net present value (NPV) is a simple and transparent
indicator of overall costs and benefits. To calculate
NPV, all future financial costs and benefits are
transformed into an equivalent current-day cost or
benefit. Future costs and benefits are discounted to
reflect the time value of money and/or the appropriate
societal discount rate. This combines all the costs and
benefits into one number, which can be thought of
as the current-day value of the entire project. If this
value is positive, it can concluded that the project is
cost-effective without consideration of the qualitative
factors.
Benefit/cost ratio is a variation on NPV where all
benefits and costs are discounted and summed
to a current-day equivalent. Then a simple ratio of
benefits to costs is calculated. If the ratio is greater
than 1.0, then it can be concluded that the project is
cost-effective without consideration of the qualitative
factors.
Cash flow analysis is a third method. The costs and
benefits that occur in each year to provide a clear
sense of the timing of the costs and benefits, and
lends itself to a graphical summary of the project’s
finances.
Stakeholder Perspectives
Including all costs and benefits in an NPV or benefitsto-costs ratio calculation implicitly assumes a societal
perspective. A stakeholder perspective, in contrast,
may intentionally exclude some costs and benefits.
The utility, for example, may not value reduced CO2
emissions, and therefore could leave the CO2 reduction
benefit out of the analysis. When performing a
stakeholder CBA, it is useful to look at the list of costs
and benefits, and exclude those that are not relevant
or not valued.
Box 9: Ruritania Smart Grid Project: Comparing Costs and Benefits
In our example, we assess the costs and benefits of smart PV inverters and advanced grid
support from wind turbines. We found eight distinct non-zero benefits of these specific
smart grid technologies (see Table 3F). The NPV of each of those benefits is shown in Table
3I.
Table 3I: NPV of benefits in example CBA
Benefit
NPV (million USD)
Reduced ancillary service cost
4.3
Deferred distribution investments
2.3
Reduced equipment failures
<0.1
Reduced electricity losses
17.6
Reduced sustained outages
28.6
Reduced restoration cost
<0.1
Reduced CO2 emissions
9.6
Reduced non-CO2 emissions
1.1
TOTAL
63.6
In Table 3H, we estimated the costs of these technologies. The NPV of those costs are
summarised in Table 3J.
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Table 3J: NPV of costs in example CBA
Cost
NPV (million USD)
Advanced inverters for PV
-23.8
Advanced inverters for wind
-14.2
TOTAL
-38.0
Note: Values shown are negative, as they are costs.
The net benefit, excluding qualitative factors, is (63.6 – 38.0) = USD 25.6 million. The
benefit-to-cost ratio is (63.6/38.0) = 1.67. This project is cost-effective.
Incorporating Qualitative Costs and Benefits
As discussed throughout this report, qualitative costs
and benefits should be tracked through the analysis.
When the final result (for example, NPV) is calculated,
it should be shown along with the list of qualitative
costs and benefits to decision-makers.
One way to incorporate these factors into the analysis
is to consider the direction and magnitude these
factors would need to change in order to alter the
result. In our example, we found that the project had
a net benefit of USD 25.6 million and further identified
workforce training as an additional qualitative benefit.
In this case, there is no need to attach a number to this
workforce training benefit, as the project is already
cost-effective without considering it.
Think about a different case, in which the proposed
project had a net benefit of USD 2.8 million (meaning
it was not cost-effective). In this case, the workforce
training benefit would need to be worth at least USD
2.8 million to change the outcome of the CBA. This
process helps to clarify and bound the qualitative
factors.
STEP 7: SENSITIVITY ANALYSIS
Once a final result, either a NPV or benefit-to-cost ratio
is obtained, it is very useful to go back and perform a
sensitivity analysis, which is an assessment of how the
results vary when various inputs and assumptions are
changed. When doing so, focus on those inputs and
assumptions that are both uncertain and significant,
meaning they strongly influence the results.
30
S M A RT G RI DS A ND R E NE WA BL E S
As shown in the example above, changing the
assumption about the value of reduced outages
changes the final result from a positive NPV to a
negative NPV. This illustrates the value of the CBA
process. If, in this example, decision-makers want to
incorporate electricity users’ valuation of the benefits,
then the project appears to be cost-effective (meaning
it has a positive NPV). If, on the other hand, decisionmakers want to take a utility perspective, then the
project does not appear to be cost-effective. (This
places no value on the qualitative benefits.) There is
no correct answer here; the conclusion depends on
what values and perspective decision-makers want to
adopt.
A similar process could be pursued for other inputs
and assumptions that are deemed uncertain and
critical. An interesting question, for example, might
be to calculate the cost assumptions that result in an
NPV of 0. If decision-makers are confident that costs
will fall below that assumption, then they could be
reasonably sure that the project overall will have a
positive NPV.
Box 10: Ruritania Smart Grid Project: Sensitivity Analysis
As shown in Table 3I, reduced sustained outages and reduced electricity losses are the
largest components of the benefits NPV. As discussed in Chapter 3, a critical assumption
in estimating the benefits of reduced sustained outages is the value of this reduction to
electricity users. The results in Table 3I assume a value of USD 3 for each kWh reduction in
sustained losses. That value is based on a meta-analysis of a large number of studies. Note,
however, that this USD 3/kWh value reflects the electricity users’ perspective. The utility,
in contrast, might value these reduced outages as worth only the regained electricity sales
they yield—that is, at the current retail rate of electricity. As shown in Table 3K, changing
the value of these outage reductions from USD 3/kWh to USD 0.10/kWh changes the NPV
from +USD 28.6 million to –USD 2.0 million.
Table 3K: Comparing value from societal and utility perspectives
Perspective
Assumed value of Resulting NPV of Final net NPV,
reduced outage
reduced sustained reflecting all
outages benefit
benefits and costs
Society
USD 3/kWh
USD 28.6 million
+USD 25.6 million
Utility
USD 0.10/kWh
USD 1.0 million
−USD 2.0 million
TOTAL
-38.0
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31
© Dainis Derics
Summary
S
mart grid projects in developing countries can enable higher levels of renewables on electricity grids,
but these projects need to be rigorously evaluated to determine if their benefits exceed their costs. CBA
defines and evaluates those costs and benefits, and can help decision-makers better allocate capital.
Defining and quantifying the benefits of a proposed smart grid project is complex and challenging. However,
breaking it down into a series of logical and ordered steps simplifies the process and clarifies the assumptions
and uncertainties.
Smart grid project costs can be estimated using published data and/or vendor estimates. The uncertainties are
generally smaller than for benefit-side estimates, although qualitative and project implementation costs are
project-specific and thus may require additional analysis.
Combining costs and benefits is a straightforward financial calculation. Qualitative costs and benefits can be
incorporated by calculating the values they would need in order to change the net benefits from positive to
negative (or from negative to positive). Similarly, sensitivity analysis can be used to show how net benefits vary
with input assumptions.
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S M A RT G RI DS A ND R E NE WA BL E S
© Pi-Lens
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33
List of
Abbreviations
AC – alternating current
JPS – Jamaica Public Service Company
AMI – advanced metering infrastructure
JRC – Joint Research Centre
C&I – commercial and industrial
K – thousand
CBA – cost-benefit analysis
kg – kilogram(s)
CCGT – combined-cycle gas turbine
km – kilometer(s)
CO2 – carbon dioxide
kVAR – kilo-volt-ampere reactive
DA – distribution automation
kW – kilowatt(s)
DC – direct current
kWh – kilowatt-hour(s)
DR – demand response
LCOF – levelised cost of flexibility
DSM – demand-side management
LV – low voltage
DOE – Department of Energy
M – million
EPRI – Electric Power Research Institute
MV – medium voltage
EU – European Union
MVAR – mega-volt-ampere reactive
GDP – gross domestic product
MVAR-hr – mega-volt-ampere reactive-hour
GW – gigawatt(s)
MW – megawatt(s)
GWh – gigawatt-hour(s)
MW-hr – megawatt-hour(s) (used for ancillary services)
hr - hour
MWh – megawatt-hour(s) (used for energy)
IEA – International Energy Agency
NOx – nitrogen oxide
IEEE – Institute of Electrical and Electronics Engineers
NPV – net present value
IITC – IRENA Innovation and Technology Centre
NRC – National Research Council
IRENA – International Renewable Energy Agency
O&M – operations and maintenance
ISO – independent system operator
34
S M A RT G RI DS A ND R E NE WA BL E S
OECD – Organisation for Economic Cooperation and
Development
PM10 – particulate matter less than 10 micrometers in
diameter
PV – photovoltaic
RE – renewable energy
SAIDI – system average interruption duration index
SAIFI – system average interruption frequency index
SE4All – International Year of Sustainable Energy for All
SCC – social cost of carbon
SOx – sulfur oxide
T&D – transmission and distribution
TOU – time of use
UN – United Nations
U.S. – United States
USD - United States dollars
VAR – volt-ampere(s) reactive
VOLL – value of lost load
volt-VAR – voltage-volt-ampere reactive
VVO – volt-VAR optimisation
yr - year
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References
Asmus, P. (2010). “Will New Technologies Boost Wind
Forecasting Accuracy?” Navigant Research. http://
www.navigantresearch.com/blog/articles/will-newtechnologies-boost-wind-forecasting-accuracy.
Joint Research Centre Reference Report, European
Commission.
ses.jrc.ec.europa.eu/sites/ses/files/
documents/guidelines_for_cost_benefit_analysis_
of_smart_metering_deployment.pdf.
Beck, R.W. (2009). “Distributed Renewable Energy
Operating Impacts & Valuation Study”. Report
to Arizona Public Service. http://files.meetup.
com/1073632/RW-Beck-Report.pdf.
HDR Engineering. (2007). “Water Meter Plan and
System Evaluation. City of Dubuque, Iowa.” http://
www.cityofdubuque.org/DocumentCenter/Home/
View/1946.
Eaton. (2014). “Power factor correction: a guide for the
plant engineer.” http://www.eaton.com/ecm/groups/
public/@pub/@electrical/documents/content/
sa02607001e.pdf.
Idaho Power Company. (2013). “Demand-Side
Management 2012 Annual Report—Supplement 1: CostEffectiveness.” Boise, ID. http://www.idahopower.
com/pdfs/AboutUs/RatesRegulatory/Reports/56.pdf.
EPRI (Electric Power Research Institute). (2010).
“Methodological Approaches for Estimating the
Benefits and Costs of Smart Grid Demonstration
Projects.” (No. 1020342). Palo Alto, CA. http://www.
osti.gov/scitech/biblio/986425.
IEA (International Energy Agency). (2013). World
Energy Outlook 2103. OECD/IEA Publishing. Paris.
Available at www.iea.org.
EPRI. (2013). Guidebook for Cost/Benefit Analysis
of Smart Grid Demonstration Projects (No.
3002002266). Retrieved from http://www.epri.com/
abstracts/Pages/ProductAbstract.aspx?Product
Id=000000003002002266.
EC (European Commission). (2012a). “Guidelines
for Conducting Cost-Benefit Analysis of Smart
Grid Projects”. Joint Research Centre Reference
Report, European Commission. http://ec.europa.eu/
energy/gas_electricity/smartgrids/doc/20120427_
smartgrids_guideline.pdf.
EC. (2012b). “Guidelines for Conducting CostBenefit Analysis of Smart Metering Deployment”.
36
S M A RT G RI DS A ND R E NE WA BL E S
IEA. (2014). The Power of Transformation—Wind, Sun
and the Economics of Flexible Power Systems. OECD/
IEA Publishing. Paris. Available at www.iea.org.
IMF (International Monetary Fund). (2003). IMF
Approves Unification of Discount Rates used in
External Debt Analysis for Low-Income Countries,
Press Release 13/408, October 18, 2003. http://www.
imf.org/external/np/sec/pr/2013/pr13408.htm.
IRENA (International Renewable Energy Agency).
(2012). “Electricity Storage and Renewables for Island
Power: A Guide for Decision Makers.” IRENA. Abu
Dhabi. May 2012. www.irena.org.
IRENA. (2013). “Smart Grids and Renewables: A Guide
for Effective Deployment.” IRENA. Abu Dhabi. http://
www.irena.org/DocumentDownloads/Publications/
smart_grids.pdf.
20 (2013) 336-352.
IRENA (2014), “REmap 2030: A Renewable Energy
Roadmap.” IRENA. Abu Dhabi. June 2014. http://irena.
org/remap/.
World Bank. (2014, February). “Nepal: Village Micro
Hydro.” The World Bank. Washington, DC. Retrieved
March 27, 2014. http://www.worldbank.org/projects/
P095978/nepal-village-micro-hydro?lang=en.
IRENA (2015a). Renewable Power Generation Costs
in 2014. IRENA. Abu Dhabi. January 2015. Retrieved
from www.irena.org/costing
IRENA. (2015b). Renewables and electricity storage.
A Technology roadmap for REmap 2030. IRENA. Abu
Dhabi. June 2015. www.irena.org/publications
Monitoring Analytics, LLC. (2013). “Quarterly State of
the Market Report for PJM: January through March.”
Eagleville, PA. http://www.monitoringanalytics.com/
reports/pjm_state_of_the_market/2013/2013q1-sompjm-sec9.pdf.
Northeast Group, LLC. (2012). “Middle East &
North Africa Smart Grid: Market Forecast (2012–
2022).” Washington, DC. http://www.northeastgroup.com/reports/MENA _Smart_Grid_Market_
Forecast_2012-2022_Brochure.pdf.
Ontario Energy Board. (2011). “Monitoring Report
Smart Meter Investment— September 2010.”
Toronto.
http://www.ontarioenergyboard.ca/oeb/_
Documents/SMdeployment/SM _Cost _ Repor t _
September2010.pdf.
OpenEI. “Transparent Cost Database.” (n.d.). Retrieved
May 20, 2014, from http://en.openei.org/apps/TCDB/.
Rudden, R., & K. Rudden. (2012). “The Value of
Sustainability: At Least $20-25 Billion for Utility
Investors (Research Note).” Target Rock Advisors.
Toronto.
http://www.targetrockadvisors.com/
research-reports/.
U.S. DOE (Department of Energy). (2006). “Benefits
of Demand Response in Electricity Markets and
Recommendations for Achieving Them.” http://energy.
gov/sites/prod/files/oeprod/DocumentsandMedia/
DOE _ Benefits _of_ Demand _ Response _in _
Electricity_Markets_and_Recommendations_for_
Achieving_Them_Report_to_Congress.pdf.
Welsch, M., M. Bazilian, M. Howells, D. Divan, D.
Elzinga, G. Strbac, L. Jones, A. Keane, D. Gielen, V.S.K.
Balijepalli, A. Brew-Hammond, and K. Yumkella, Smart
and Just Grids for sub-Saharan Africa: Exploring
Options, Renewable and Sustainable Energy Reviews
A C O S T-BE N E F IT AN ALYSIS GU IDE F O R DEVE LO PIN G CO U NTRI ES
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This report is supported by a number of annexations, which are available online.
Annex I: Ruritania Case Study: Distribution Automation Programme with no Predefined Renewables Goal
Approach
Annex II: Jamaica Case Study: Demand Response Programme With a Pre-Defined Renewables Goal
Annex III: Methods of Benefit Valuation
Annex IV: List of Smart Grid technologies for Renewable
Annex V: Smart Grids for Renewables: Real Case Studies
Annex VI: Glossary
Annex VII: Bibliography
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I R ENA 2015